from model import Model
from pySAXS.LS.LSsca import Qlogspace
from pySAXS.LS.LSsca import getV
from pySAXS.LS.LSsca import P1
import numpy
[docs]class MonoSphere(Model):
'''
class monoSphere from LSSca
by OT 10/06/2009
'''
[docs] def MonoSphereFunction(self,q,par):
"""
q array of q (A-1)
par[0] radius of the sphere (A)
par[1] scattering length density of sphere (cm-2)
par[2] scattering length density of outside (cm-2)
par[3] concentration of sphere (cm-3)
"""
if len(par)!=4:
sys.stderr.write("This function requires a list of 4 parameters")
return -1.
else:
return par[3]*(par[1]-par[2])**2.*getV(par[0])*getV(par[0])*1e-48*P1(q,par[0])
#sys.stderr.write(str(par[0]))
#return P1(q,par[0])
'''
parameters definition
Model(0,MonoSphere,Qlogspace(1e-4,1.,500.),([250.0,2e11,1e10,1.5e15]),
("radius (A)","scattering length density of sphere (cm-2)","scattering length density of outside (cm-2)","number concentration (cm-3)")
,("%f","%1.3e","%1.3e","%1.3e"),(True,True,False,False)),
from LSsca
'''
IntensityFunc=MonoSphereFunction #function
N=0
q=Qlogspace(1e-4,1.,500.) #q range(x scale)
Arg=[30.0,9.8e11,9.8e10,1.e10] #list of defaults parameters
Format=["%f","%1.3e","%1.3e","%1.3e"] #list of c format
istofit=[True,True,False,False] #list of boolean for fitting
name="Spheres Monodisperse" #name of the model
Doc=["radius (A)",\
"scattering length density of sphere (cm-2)",\
"scattering length density of medium (cm-2)",\
"number concentration (cm-3)"] #list of description for parameters
if __name__=="__main__":
'''
test code
'''
modl=MonoSphere()
#plot the model
import Gnuplot
gp=Gnuplot.Gnuplot()
gp("set logscale xy")
c=Gnuplot.Data(modl.q,modl.getIntensity(),with_='points')
gp.plot(c)
raw_input("enter")
#plot and fit the noisy model
yn=modl.getNoisy(0.4)
cn=Gnuplot.Data(modl.q,yn,with_='points')
res=modl.fit(yn)
cf=Gnuplot.Data(modl.q,modl.IntensityFunc(modl.q,res),with_='lines')
gp.plot(c,cn,cf)
raw_input("enter")
#plot and fit the noisy model with fitBounds
bounds=modl.getBoundsFromParam() #[250.0,2e11,1e10,1.5e15]
res2=modl.fitBounds(yn,bounds)
print res2
raw_input("enter")